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no code implementations • ICLR 2022 • Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams

Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards.

1 code implementation • NeurIPS 2021 • Erik Henning Thiede, Wenda Zhou, Risi Kondor

Our formalism also encompasses novel architectures: as an example, we introduce a graph neural network that decomposes the graph into paths and cycles.

1 code implementation • 16 Jul 2020 • Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams

Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design.

1 code implementation • 3 Mar 2020 • Kamiar Rahnama Rad, Wenda Zhou, Arian Maleki

We study the problem of out-of-sample risk estimation in the high dimensional regime where both the sample size $n$ and number of features $p$ are large, and $n/p$ can be less than one.

1 code implementation • NeurIPS 2019 • Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams

The success of generative modeling in continuous domains has led to a surge of interest in generating discrete data such as molecules, source code, and graphs.

1 code implementation • 4 Oct 2018 • Shuaiwen Wang, Wenda Zhou, Arian Maleki, Haihao Lu, Vahab Mirrokni

$\mathcal{C} \subset \mathbb{R}^{p}$ is a closed convex set.

2 code implementations • ICML 2018 • Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni

Consider the following class of learning schemes: $$\hat{\boldsymbol{\beta}} := \arg\min_{\boldsymbol{\beta}}\;\sum_{j=1}^n \ell(\boldsymbol{x}_j^\top\boldsymbol{\beta}; y_j) + \lambda R(\boldsymbol{\beta}),\qquad\qquad (1) $$ where $\boldsymbol{x}_i \in \mathbb{R}^p$ and $y_i \in \mathbb{R}$ denote the $i^{\text{th}}$ feature and response variable respectively.

1 code implementation • 27 Jun 2018 • Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz

We solve this problem using recent ideas from graph sampling theory to (i) define an empirical risk for relational data and (ii) obtain stochastic gradients for this empirical risk that are automatically unbiased.

1 code implementation • ICLR 2019 • Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

Our main technical result is a generalization bound for compressed networks based on the compressed size.

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